---
title: Caching
hide_title: true
sidebar_position: 5
version: 1
---

## Caching

Superset uses [Flask-Caching](https://flask-caching.readthedocs.io/) for caching purposes. Configuring caching is as easy as providing a custom cache config in your
`superset_config.py` that complies with [the Flask-Caching specifications](https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching).
Flask-Caching supports various caching backends, including Redis, Memcached, SimpleCache (in-memory), or the
local filesystem. Custom cache backends are also supported. See [here](https://flask-caching.readthedocs.io/en/latest/#custom-cache-backends) for specifics.
The following cache configurations can be customized:
- Metadata cache (optional): `CACHE_CONFIG`
- Charting data queried from datasets (optional): `DATA_CACHE_CONFIG`
- SQL Lab query results (optional): `RESULTS_BACKEND`. See [Async Queries via Celery](/docs/installation/async-queries-celery) for details
- Dashboard filter state (required): `FILTER_STATE_CACHE_CONFIG`.
- Explore chart form data (required): `EXPLORE_FORM_DATA_CACHE_CONFIG`

Please note, that Dashboard and Explore caching is required. If these caches are undefined, Superset falls back to using a built-in cache that stores data
in the metadata database. While it is recommended to use a dedicated cache, the built-in cache can also be used to cache other data.
For example, to use the built-in cache to store chart data, use the following config:

```python
DATA_CACHE_CONFIG = {
    "CACHE_TYPE": "SupersetMetastoreCache",
    "CACHE_KEY_PREFIX": "superset_results",  # make sure this string is unique to avoid collisions
    "CACHE_DEFAULT_TIMEOUT": 86400,  # 60 seconds * 60 minutes * 24 hours
}
```

- Redis (recommended): we recommend the [redis](https://pypi.python.org/pypi/redis) Python package
- Memcached: we recommend using [pylibmc](https://pypi.org/project/pylibmc/) client library as
  `python-memcached` does not handle storing binary data correctly.

Both of these libraries can be installed using pip.

For chart data, Superset goes up a “timeout search path”, from a slice's configuration
to the datasource’s, the database’s, then ultimately falls back to the global default
defined in `DATA_CACHE_CONFIG`.

## Celery beat

### Caching Thumbnails

This is an optional feature that can be turned on by activating it’s [feature flag](https://superset.apache.org/docs/installation/configuring-superset#feature-flags) on config:

```
FEATURE_FLAGS = {
    "THUMBNAILS": True,
    "THUMBNAILS_SQLA_LISTENERS": True,
}
```

For this feature you will need a cache system and celery workers. All thumbnails are stored on cache
and are processed asynchronously by the workers.

An example config where images are stored on S3 could be:

```python
from flask import Flask
from s3cache.s3cache import S3Cache

...

class CeleryConfig(object):
    broker_url = "redis://localhost:6379/0"
    imports = ("superset.sql_lab", "superset.tasks", "superset.tasks.thumbnails")
    result_backend = "redis://localhost:6379/0"
    worker_prefetch_multiplier = 10
    task_acks_late = True


CELERY_CONFIG = CeleryConfig

def init_thumbnail_cache(app: Flask) -> S3Cache:
    return S3Cache("bucket_name", 'thumbs_cache/')


THUMBNAIL_CACHE_CONFIG = init_thumbnail_cache
# Async selenium thumbnail task will use the following user
THUMBNAIL_SELENIUM_USER = "Admin"
```

Using the above example cache keys for dashboards will be `superset_thumb__dashboard__{ID}`. You can
override the base URL for selenium using:

```
WEBDRIVER_BASEURL = "https://superset.company.com"
```

Additional selenium web drive configuration can be set using `WEBDRIVER_CONFIGURATION`. You can
implement a custom function to authenticate selenium. The default function uses the `flask-login`
session cookie. Here's an example of a custom function signature:

```python
def auth_driver(driver: WebDriver, user: "User") -> WebDriver:
    pass
```

Then on configuration:

```
WEBDRIVER_AUTH_FUNC = auth_driver
```
